Optimizing Distribution Network Reconfiguration to Minimize the Time Dial Setting of Overcurrent Relays
محورهای موضوعی : مهندسی هوشمند برقEsmail Ahmadi 1 , Mohsen Simab 2 , Bahman Bahmani-Firouzi 3
1 - Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
2 - Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
3 - Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
کلید واژه: Reconfiguration, Modified Sine Cosine Algorithm, Time Dial Setting, Overcurrent Relays.,
چکیده مقاله :
This paper explores the optimization of distribution network reconfiguration to minimize the time dial setting of overcurrent relays, essential for improving protection and reliability in power systems. The study focuses on employing advanced optimization techniques to enhance network efficiency. Specifically, the proposed modified sine cosine algorithm (MSCA) is investigated and compared against conventional methods. Results demonstrate that MSCA consistently achieves superior performance across critical metrics including significant reductions in power losses, improved system stability, and optimized voltage profiles. For instance, MSCA achieves a notable 16% reduction in power losses and enhances system stability by 22%, demonstrating its effectiveness in optimizing network operation. Simulations conducted on both 33-bus and 119-bus IEEE network configurations validate the robustness and versatility of MSCA in diverse network environments. These findings underscore the practical applicability of MSCA in modern power systems, offering tangible benefits in terms of efficiency, reliability, and operational flexibility. The research provides valuable insights for power system engineers and researchers seeking to implement advanced optimization strategies to address challenges in network reconfiguration and enhance overall system performance.
This paper explores the optimization of distribution network reconfiguration to minimize the time dial setting of overcurrent relays, essential for improving protection and reliability in power systems. The study focuses on employing advanced optimization techniques to enhance network efficiency. Specifically, the proposed modified sine cosine algorithm (MSCA) is investigated and compared against conventional methods. Results demonstrate that MSCA consistently achieves superior performance across critical metrics including significant reductions in power losses, improved system stability, and optimized voltage profiles. For instance, MSCA achieves a notable 16% reduction in power losses and enhances system stability by 22%, demonstrating its effectiveness in optimizing network operation. Simulations conducted on both 33-bus and 119-bus IEEE network configurations validate the robustness and versatility of MSCA in diverse network environments. These findings underscore the practical applicability of MSCA in modern power systems, offering tangible benefits in terms of efficiency, reliability, and operational flexibility. The research provides valuable insights for power system engineers and researchers seeking to implement advanced optimization strategies to address challenges in network reconfiguration and enhance overall system performance.
1. Behinnezhad, S. M., Shahgholian, G., & Fani, B. (2023). Simulation of a PV connected to an electrical energy distribution network with internal current loop control and voltage regulator. International Journal of Smart Electrical Engineering, 12(01), 23-30.
2. Mortazi, A., Saeed, S., & Akbari, H. (2023). Optimizing Operation Scheduling in a Microgrid Considering Probabilistic Uncertainty and Demand Response Using Social Spider Algorithm. International Journal of Smart Electrical Engineering, 12(02), 113-125.
3. Riahinasab, M., Dehghani, M., Fathollahi, A., & Behzadfar, M. R. (2023). A Brief Overview of the Application of Unified Power Flow Controller in Power Systems. International Journal of Smart Electrical Engineering, 12(03), 183-191.
4. Ushashree, P., & Kumar, K. S. (2023). Power system reconfiguration in distribution system for loss minimization using optimization techniques: a review. Wireless Personal Communications, 128(3), 1907-1940.
5. Mahdavi, M., Schmitt, K. E. K., & Jurado, F. (2023). Robust distribution network reconfiguration in the presence of distributed generation under uncertainty in demand and load variations. IEEE Transactions on Power Delivery, 38(5), 3480-3495.
6. Ma, R., Chai, X., Geng, R., Xu, L., Xie, R., Zhou, Y., ... & Gao, F. (2023). Recent progress and challenges of multi-stack fuel cell systems: Fault detection and reconfiguration, energy management strategies, and applications. Energy Conversion and Management, 285, 117015.
7. Hosseini, S., Gharehpetian, G. B., Mozafari, S. B., & Vatani, M. (2024). A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches. Sustainable Cities and Society, 108, 105489.
8. Azizi, A., Vahidi, B., & Nematollahi, A. F. (2023). Reconfiguration of active distribution networks equipped with soft open points considering protection constraints. Journal of Modern Power Systems and Clean Energy, 11(1), 212-222.
9. Imteaj, A., Akbari, V., & Amini, M. H. (2023). A novel MSCAlable reconfiguration model for the postdisaster network connectivity of resilient power distribution systems. Sensors, 23(3), 1200.
10. Cikan, N. N., & Cikan, M. (2024). Reconfiguration of 123-bus unbalanced power distribution network analysis by considering minimization of current & voltage unbalanced indexes and power loss. International Journal of Electrical Power & Energy Systems, 157, 109796.
11. Fathi, R., Tousi, B., & Galvani, S. (2023). Allocation of renewable resources with radial distribution network reconfiguration using improved salp swarm algorithm. Applied Soft Computing, 132, 109828.
12. Stojanović, B., Rajić, T., & Šošić, D. (2023). Distribution network reconfiguration and reactive power compensation using a hybrid Simulated Annealing–Minimum spanning tree algorithm. International Journal of Electrical Power & Energy Systems, 147, 108829.
13. Khalid, H., & Shobole, A. (2021). Existing developments in adaptive smart grid protection: A review. Electric Power Systems Research, 191, 106901.
14. Aminifar, F., Abedini, M., Amraee, T., Jafarian, P., Samimi, M. H., & Shahidehpour, M. (2022). A review of power system protection and asset management with machine learning techniques. Energy Systems, 13(4), 855-892.
15. Xu, C. H. U., & Zehong, B. A. O. (2023). Overview of protection principle of power grid in integrated energy system. Journal of Shanghai Jiaotong University, 57(4), 379.
16. Ufa, R. A., Malkova, Y. Y., Rudnik, V. E., Andreev, M. V., & Borisov, V. A. (2022). A review on distributed generation impacts on electric power system. International journal of hydrogen energy, 47(47), 20347-20361.
17. Zakariya, M. Z., & Teh, J. (2023). A systematic review on caMSCAding failures models in renewable power systems with dynamics perspective and protections modeling. Electric Power Systems Research, 214, 108928.
18. Nazir, M., Burkes, K., & Enslin, J. H. (2020). Converter-based solutions: Opening new avenues of power system protection against solar and hemp mhd-e3 gic. IEEE Transactions on Power Delivery, 36(4), 2542-2549.
19. Agbetuyi, A. F., Bango, O., Abdulkareem, A., Awelewa, A., Somefun, T., Olubunmi, A., & Oluranti, A. (2021). Investigation of the impact of distributed generation on power system protection. Adv. Sci. Technol. Eng. Syst. J, 6(2), 324-331.
20. Devabalaji, K. R., & Ravi, K. (2016). Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm. Ain Shams Engineering Journal, 7(3), 959-971.
21. Dima Imad Kayyali, (2014), An Optimal Integrated Approach Considering Distribution System Reconfiguration and Protection Coordination, Master of Science, Masdar Institute of Science and Technology.
